// CHECK-DAG: ml_program.global public @vars.Variable_1([]) : tensor<!tf_type.string>
  func.func @lowers_string_ops(%arg0: tensor<128xi32>, %arg1: tensor<128xi32>, %arg2: tensor<128x1xi32>, %arg3: tensor<128x90xi32>, %arg4: tensor<128x90xi32>, %arg5: tensor<128x90xi32>, %arg6: tensor<128x90x64xf32>, %arg7: tensor<128x90x64xf32>) -> tensor<!tf_type.string> {
    // CHECK: %[[v0:.*]] = ml_program.global_load @vars.Variable_1 : tensor<!tf_type.string>

  func.return %4 : tensor<128x128x!quant.uniform<u8:f32, 0.2:125>>

// CHECK: %4 = "tfl.custom_tf"(%arg0, %arg1, %arg2, %arg3) ({
// CHECK-NEXT: ^bb0(%arg4: tensor<128x128xf32>, %arg5: tensor<1xf32>, %arg6: tensor<1xf32>, %arg7: tensor<1xi32>):

// CHECK-LABEL: func @FusedBatchNormRelu
func.func @FusedBatchNormRelu(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>) -> (tensor<8x8x8x8xf32>) {
// CHECK-NEXT: %[[Y:[a-z0-9]*]], {{.*}}_FusedBatchNormEx
// CHECK-NEXT: return %[[Y]]

// CHECK: func private @tf.IfRegion_else(%arg0: tensor<i1>, %arg1: tensor<2xf32>, %arg2: tensor<2xf32>, %arg3: tensor<2xf32>) -> tensor<2xf32>
// CHECK-NEXT: "tf.Select"(%arg0, %arg2, %arg3)
// CHECK: func private @tf.IfRegion_then(%arg0: tensor<i1>, %arg1: tensor<2xf32>, %arg2: tensor<2xf32>, %arg3: tensor<2xf32>) -> tensor<2xf32>
// CHECK-NEXT: "tf.Select"(%arg0, %arg1, %arg2)
func.func @testIfCondition(%arg0: tensor<i1>, %arg1: tensor<2xf32>) -> tensor<2xf32> {

// CHECK-LABEL: func @__forward_call_369
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg1:%.*]]: !tfrt_fallback.tf_tensor {tf._user_specified_name = "inputs"},
// CHECK-SAME: [[arg2:%.*]]: !tfrt_fallback.tf_tensor, [[arg3:%.*]]: !tfrt_fallback.tf_tensor, [[arg4:%.*]]: !tfrt_fallback.tf_tensor, [[arg5:%.*]]: !tfrt_fallback.tf_tensor)
// CHECK-SAME: -> (!tfrt.chain
// CHECK: [[o1:%.*]] = tfrt_fallback_async.const_dense_tensor

// CHECK-SAME: (%[[ARG0:.*]]: tensor<f32>, %[[ARG1:.*]]: tensor<f32>, %[[ARG2:.*]]: tensor<*xf32>)
func.func @test_supported_lowering_of_tf_while_region(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<*xf32>) -> (tensor<f32>){
  %0:2 = "tf.WhileRegion"(%arg0, %arg2) ({
  ^bb0(%arg3: tensor<f32>, %arg4: tensor<*xf32>):
    %1 = "tf.Identity"(%arg3) : (tensor<f32>) -> tensor<f32>

  %4:2 = "mhlo.reduce"(%arg0, %3, %0, %1) ({
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<i32>):
    %7 = "mhlo.compare"(%arg1, %arg3) {comparison_direction = #mhlo<comparison_direction GT>} : (tensor<f32>, tensor<f32>) -> tensor<i1>
    %8 = "mhlo.compare"(%arg1, %arg1) {comparison_direction = #mhlo<comparison_direction NE>} : (tensor<f32>, tensor<f32>) -> tensor<i1>

// CHECK:           return %[[VAL_4]] : tensor<2x3xf16>
// CHECK:         }
func.func @floordiv_f16_broadcast(%arg0: tensor<2x3xf16>, %arg1: tensor<3xf16>) -> tensor<2x3xf16> {
  %0 = "chlo.broadcast_divide"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>
  %1 = "chlo.broadcast_divide"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>

  // CHECK: %[[OUT:.*]], %[[MEAN:.*]], %[[VAR:.*]] = "mhlo.batch_norm_training"({{.*}}, %arg1, %arg2) <{epsilon = 1.000000e-03 : f32, feature_index = 3 : i64}> : (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>)

// CHECK: return %[[conv_quant]]

// CHECK-LABEL: func private @quantized_conv2d_with_bias_and_relu6_float_output_fn_0
// CHECK-SAME: (%arg0: tensor<1x2x2x3xi8>, %arg1: tensor<2x2x3x2xi8>, %arg2: tensor<2xi32>, %arg3: tensor<f32>, %arg4: tensor<i32>, %arg5: tensor<2xf32>, %arg6: tensor<2xi32>, %arg7: tensor<2xf32>, %arg8: tensor<2xi32>, %arg9: tensor<f32>, %arg10: tensor<i32>) -> tensor<*xf32>
// CHECK:      %[[CONV2D_0:.*]] = "tf.Conv2D"